4 citations · 10 across the 12 of their papers we have counts for
8 papers · 1 filter
Equalized Generative Treatment: Matching f-divergences for Fairness in Generative Models
Alexandre Verine, Rafael Pinot, Florian Le Bronnec
Fairness is a crucial concern for generative models, which not only reflect but can also amplify societal and cultural biases. Existing fairness notions for generative models are l…
Learning To Sample From Diffusion Models Via Inverse Reinforcement Learning
Constant Bourdrez, Alexandre Vérine, Olivier Cappé
Diffusion models generate samples through an iterative denoising process guided by a pretrained neural network. Once the denoiser is fixed, the sampling algorithm itself (noise sch…
Improving Discriminator Guidance in Diffusion Models
Alexandre Verine, Ahmed Mehdi Inane, Florian Le Bronnec +2
Discriminator Guidance has become a popular method for efficiently refining pre-trained Score-Matching Diffusion models. However, in this paper, we demonstrate that the standard im…
Conditional Distribution Quantization in Machine Learning
Blaise Delattre, Sylvain Delattre, Alexandre Vérine +1
Conditional expectation \mathbb{E}(Y \mid X) often fails to capture the complexity of multimodal conditional distributions \mathcal{L}(Y \mid X). To address this, we propose using…
Optimal Budgeted Rejection Sampling for Generative Models
Alexandre Verine, Muni Sreenivas Pydi, Benjamin Negrevergne +1
Rejection sampling methods have recently been proposed to improve the performance of discriminator-based generative models. However, these methods are only optimal under an unlimit…
Precision-Recall Divergence Optimization for Generative Modeling with GANs and Normalizing Flows
Alexandre Verine, Benjamin Negrevergne, Muni Sreenivas Pydi +1
Achieving a balance between image quality (precision) and diversity (recall) is a significant challenge in the domain of generative models. Current state-of-the-art models primaril…
Training Normalizing Flows with the Precision-Recall Divergence
Alexandre Verine, Benjamin Negrevergne, Muni Sreenivas Pydi +1
Generative models can have distinct mode of failures like mode dropping and low quality samples, which cannot be captured by a single scalar metric. To address this, recent works p…
On the expressivity of bi-Lipschitz normalizing flows
Alexandre Verine, Benjamin Negrevergne, Fabrice Rossi +1
An invertible function is bi-Lipschitz if both the function and its inverse have bounded Lipschitz constants. Nowadays, most Normalizing Flows are bi-Lipschitz by design or by trai…